So, in our example, the actual or expected output is:

Ya = [False, False, True, True, True, False, True, False, False, True]

And the predicted output is:

Y = [True, False, True, True, False, False, True, False, False, True]

So, the total number of:

True Negative = the output labels that are predicted to be False and they are actually False also = 4

False Positive = the output labels that are predicted to be True but they are actually False = 1

False Negative = output labels that are predicted to be False but they are actually True = 1

True Positive = output labels that are predicted to be True and they are actually True = 4

So, the confusion matrix will be like the following:

How to calculate Confusion Matrix using the sklearn Python library?

We can use the following Python code to compute the confusion matrix.

from sklearn.metrics import confusion_matrix

Ya = [False, False, True, True, True, False, True, False, False, True]
Y = [True, False, True, True, False, False, True, False, False, True]

conf_matrix = confusion_matrix(Ya, Y)
print("Confusion Matrix: \n", conf_matrix)

Here, Ya is the actual or expected output and Y is the predicted output. The output of the above program will be:

Confusion Matrix: 
 [[4 1]
 [1 4]]